1 Introduction

Team 010100 are the following members: Obumneke Amadi, Izzy Illari, Lucia Illari, Omar Qusous, and Lydia Teinfalt. You may find our work over on GitHub.

With the 2020 Olympics beginning this July in Tokyo we felt that a relevant discussion to have would be What makes an Olympian? What can we say about Olympians? Have there been any general trends amongst Olympians? What does the Olympic population look like? These questions are all suited to EDA, and with these questions in mind we went to see if we could find data on Olympians that would be readily available for us to analyze. Eventually our question morphed into the following: are there any specific characteristics (i.e. age, weight, height, BMI, country of origin) that could be used to describe an Olympian in general?

We were able to find a dataset called 120 years of Olympic history: athletes and results on Kaggle over here: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results. This historical dataset includes all Olympic Games from Athens 1896 to Rio 2016, which was scraped from https://www.sports-reference.com/. This data was compiled by a group of Olympic historians and statisticians. All of these individuals are members of the International Society of Olympic Historians (ISOH) and have been working on this project since the late 1990s.

The report is organized as follows:

1. Summary of Dataset
2. Description of Data
3. What are the body types of Olympic Athletes? (BMI Data)
4. Where do Olympians come from? (Geographical Data)
5. GDP Data Goes Here?
6. Are there any common names among Olympians? (Name Data)
7. Have there been any body trends among Olympians over the years? (Trends Over Time)
8. Are Olympic Athletes a certain age? (Age Data)
9. Discussion And Conclusion

2 Summary of Dataset

The data looks like the following:

'data.frame':   271116 obs. of  15 variables:
 $ ID    : int  1 2 3 4 5 5 5 5 5 5 ...
 $ Name  : Factor w/ 134732 levels "  Gabrielle Marie \"Gabby\" Adcock (White-)",..: 8 9 44318 29412 21469 21469 21469 21469 21469 21469 ...
 $ Sex   : Factor w/ 2 levels "F","M": 2 2 2 2 1 1 1 1 1 1 ...
 $ Age   : int  24 23 24 34 21 21 25 25 27 27 ...
 $ Height: int  180 170 NA NA 185 185 185 185 185 185 ...
 $ Weight: num  80 60 NA NA 82 82 82 82 82 82 ...
 $ Team  : Factor w/ 1184 levels "30. Februar",..: 199 199 273 278 705 705 705 705 705 705 ...
 $ NOC   : Factor w/ 230 levels "AFG","AHO","ALB",..: 42 42 56 56 146 146 146 146 146 146 ...
 $ Games : Factor w/ 51 levels "1896 Summer",..: 38 49 7 2 37 37 39 39 40 40 ...
 $ Year  : int  1992 2012 1920 1900 1988 1988 1992 1992 1994 1994 ...
 $ Season: Factor w/ 2 levels "Summer","Winter": 1 1 1 1 2 2 2 2 2 2 ...
 $ City  : Factor w/ 42 levels "Albertville",..: 6 18 3 27 9 9 1 1 17 17 ...
 $ Sport : Factor w/ 66 levels "Aeronautics",..: 9 33 25 62 54 54 54 54 54 54 ...
 $ Event : Factor w/ 765 levels "Aeronautics Mixed Aeronautics",..: 160 398 349 710 623 619 623 619 623 619 ...
 $ Medal : Factor w/ 3 levels "Bronze","Gold",..: NA NA NA 2 NA NA NA NA NA NA ...

The athlete events data has 15 columns and 271116 rows/entries, for a total of 4066740 individual data points. In athelete_events each row corresponds to an individual athlete competing in an individual Olympic event. The variables are the following:

1. ID: Unique number for each athlete
2. Name: Athlete’s name
3. Sex: M or F
4. Age: Integer
5. Height: centimeters
6. Weight: kilograms
7. Team: Team name
8. NOC: National Olympic Committee 3-letter code
9. Games: Year and season
10. Year: Integer
11. Season: Summer or Winter
12. City: Host city
13. Sport
14. Event
15. Medal: Gold, Silver, Bronze, or NA

To prepare our data for EDA we dropped the Olympic event: Art Sculpting. NAs were also removed.

3 Description of Data

We can look at the top events by number of athetes participating in these events. We can show this in a table or in a bar chart.

4 BMI Data

Body Mass Index (BMI) is used to group individuals into weight categories that may lead to health problems. Olympic athletes represent top physical fitness so we wanted to see if Olympians have healthy weight according to the CDC.

BMI is calculated using athlete’s weight in kilograms divided by the square of their height in meters. The dataset stored height in centimeters so the formula was modified to convert their height to meter. The resulting number can be used to classify athtletes older than 20 into one of these groups: underweight, normal or healthy weight, overweight, and obese.

               Df Sum Sq Mean Sq F value              Pr(>F)    
Sport          14  16916    1208     205 <0.0000000000000002 ***
Residuals   18632 109780       6                                
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5 Geographical Data

6 GDP Data

7 Name Data

8 Trends Over Time

We wanted to see the changes in Olympians for the top 10 events (in terms of how many Olympians were recorded for the sport) over the years. Below are the top 10 events:

It seems that in general events like rowing or basketball have been trending towards larger and larger athletes (in terms of both weight and height) whereas events like Gymnastics skews in the completely opposite direction, favoring smaller and smaller athletes. The Wrestling event data is a bit misleading—we have taken the averages per Olympic game year, and if we were to look at the Wrestling data by itself we would see clear categories for weight and height. This is due to the nature of the sport, and its weight classes. This is why in the men’s data it looks like the data for the Wrestling is a constant straight line. It appears that way because there the following weight classes at the Olympics: Freestyle weight classes (12): Men’s 57kg, Men’s 65kg, Men’s 74kg, Men’s 86kg, Men’s 97kg, Men’s 125kg, Women’s 50kg, Women’s 53kg, Women’s 57kg, Women’s 62kg, Women’s 68kg, Women’s 76kg; and then Greco-Roman weight classes (6): Men’s 60kg, Men’s 67kg, Men’s 77kg, Men’s 87kg, Men’s 97kg, Men’s 130kg.

What we can also use this over time data for is to see trends during “decades”. We can overlay the data for the decades ontop of each other on a histogram, or we could use boxplots where we’ve made a decade into a “rank”/factorial data type.

Now that I’ve created a new comlumn Decade in the dataframe, I can make histograms or boxplots of the Weight and Height and group by Decade.

We have the boxplots, but we can use ANOVA to test the difference between the means in the weight and height data over the decades. ANOVA testing we have that:

1. Null hypothesis: the means of the different groups are the same
2. Alternative hypothesis: At least one sample mean is not equal to the others.

In one-way ANOVA test, a significant \(p\)-value indicates that some of the group means are different, but we don’t know which pairs of groups are different. We use a significance level of \(\alpha = 0.05\).

Here is the ANOVA test for the weight data over the decades

               Df  Sum Sq Mean Sq F value Pr(>F)  
Decade         12    4547     379    1.68  0.063 .
Residuals   30168 6790473     225                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We can see that for aov(Weight ~ Decade, data=athlete_events_noNA), we have \(p\) = 6.341e-02. The \(p\)-value here is not significant, which means that over time, the mean weight of the athletes is not significantly different between the decades.

Here is the ANOVA test for the height data over the decades

               Df  Sum Sq Mean Sq F value           Pr(>F)    
Decade         12   10066     839    7.05 0.00000000000058 ***
Residuals   30168 3591551     119                             
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We can see that for aov(Height ~ Decade, data=athlete_events_noNA), we have \(p\) = 5.807e-13. The \(p\)-value here is very significant, which means that over time, the mean height of the athletes is significantly different between the decades.

9 Age Data

It appears that the mean age that an Olympic Medalist wins a medal is around 26 years old. We can also look at the ages of medal-winning athletes separated by the Summer and Winter Games.

From the plots we can see that between WW1 and WW2 the average age of medalists is decreasing, but after WW2 the average age temporarily rose. We see that the age begins to decrease until 1980 but then rises again after 1980. The age seems to plateau in the 2010s.

It seems that there are fewer peaks and dips in the Winter Games data than in the Summer Games data, where the Winter Athletes seem to have a smaller variance in age. We can look at the Summer and Winter Games together.

It appears that after the 1950s the athletes at the Winter Games, on average, are older. Both Summer and Winter Games experience an upward trend in ages after the 1980s.

We can also look at the breakdown of ages between Season and Gender.

When we look at the medal-winning athletes during the Summer Games by gender we see that in general men get medals at older ages than women do. Just as with the Summer Games we see that, on average, male athletes tend to be older than female athletes.

10 Discussion & Conclusion